Computational Intelligence and Neuroscience / 2019 / Article / Tab 1

Research Article

An Improved Grey Wolf Optimization Algorithm with Variable Weights

Table 1

Pseudocode of the GWO algorithm.


Set up optimizationDimension of the given problems
Limitations of the given problems
Population size
Controlling parameter
Stop criterion (maximum iteration times or admissible errors)

InitializationPositions of all of the grey wolves including α, β, and δ wolves

SearchingWhile not the stop criterion, calculate the new fitness function
Update the positions
Limit the scope of positions
Refresh α, β, and δ
Update the stop criterion

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